Utility and limitations of exome sequencing as a genetic diagnostic tool for children with hearing loss.

PURPOSE: Hearing loss (HL) is the most common sensory disorder in children. Prompt molecular diagnosis may guide screening and management, especially in syndromic cases when HL is the single presenting feature. Exome sequencing (ES) is an appealing diagnostic tool for HL as the genetic causes are highly heterogeneous. METHODS: ES was performed on a prospective cohort of 43 probands with HL. Sequence data were analyzed for primary and secondary findings. Capture and coverage analysis was performed for genes and variants associated with HL. RESULTS: The diagnostic rate using ES was 37.2%, compared with 15.8% for the clinical HL panel. Secondary findings were discovered in three patients. For 247 genes associated with HL, 94.7% of the exons were targeted for capture and 81.7% of these exons were covered at 20× or greater. Further analysis of 454 randomly selected HL-associated variants showed that 89% were targeted for capture and 75% were covered at a read depth of at least 20×. CONCLUSION: ES has an improved yield compared with clinical testing and may capture diagnoses not initially considered due to subtle clinical phenotypes. Technical challenges were identified, including inadequate capture and coverage of HL genes. Additional considerations of ES include secondary findings, cost, and turnaround time.

Characterizing reduced coverage regions through comparison of exome and genome sequencing data across 10 centers.

PURPOSE: As massively parallel sequencing is increasingly being used for clinical decision making, it has become critical to understand parameters that affect sequencing quality and to establish methods for measuring and reporting clinical sequencing standards. In this report, we propose a definition for reduced coverage regions and describe a set of standards for variant calling in clinical sequencing applications. METHODS: To enable sequencing centers to assess the regions of poor sequencing quality in their own data, we optimized and used a tool (ExCID) to identify reduced coverage loci within genes or regions of particular interest. We used this framework to examine sequencing data from 500 patients generated in 10 projects at sequencing centers in the National Human Genome Research Institute/National Cancer Institute Clinical Sequencing Exploratory Research Consortium. RESULTS: This approach identified reduced coverage regions in clinically relevant genes, including known clinically relevant loci that were uniquely missed at individual centers, in multiple centers, and in all centers. CONCLUSION: This report provides a process road map for clinical sequencing centers looking to perform similar analyses on their data.

The next generation of target capture technologies - large DNA fragment enrichment and sequencing determines regional genomic variation of high complexity.

BACKGROUND: The ability to capture and sequence large contiguous DNA fragments represents a significant advancement towards the comprehensive characterization of complex genomic regions. While emerging sequencing platforms are capable of producing several kilobases-long reads, the fragment sizes generated by current DNA target enrichment technologies remain a limiting factor, producing DNA fragments generally shorter than 1 kbp. The DNA enrichment methodology described herein, Region-Specific Extraction (RSE), produces DNA segments in excess of 20 kbp in length. Coupling this enrichment method to appropriate sequencing platforms will significantly enhance the ability to generate complete and accurate sequence characterization of any genomic region without the need for reference-based assembly. RESULTS: RSE is a long-range DNA target capture methodology that relies on the specific hybridization of short (20-25 base) oligonucleotide primers to selected sequence motifs within the DNA target region. These capture primers are then enzymatically extended on the 3'-end, incorporating biotinylated nucleotides into the DNA. Streptavidin-coated beads are subsequently used to pull-down the original, long DNA template molecules via the newly synthesized, biotinylated DNA that is bound to them. We demonstrate the accuracy, simplicity and utility of the RSE method by capturing and sequencing a 4 Mbp stretch of the major histocompatibility complex (MHC). Our results show an average depth of coverage of 164X for the entire MHC. This depth of coverage contributes significantly to a 99.94 % total coverage of the targeted region and to an accuracy that is over 99.99 %. CONCLUSIONS: RSE represents a cost-effective target enrichment method capable of producing sequencing templates in excess of 20 kbp in length. The utility of our method has been proven to generate superior coverage across the MHC as compared to other commercially available methodologies, with the added advantage of producing longer sequencing templates amenable to DNA sequencing on recently developed platforms. Although our demonstration of the method does not utilize these DNA sequencing platforms directly, our results indicate that the capture of long DNA fragments produce superior coverage of the targeted region.

Exome sequencing analysis reveals variants in primary immunodeficiency genes in patients with very early onset inflammatory bowel disease.

BACKGROUND & AIMS: Very early onset inflammatory bowel disease (VEO-IBD), IBD diagnosed at 5 years of age or younger, frequently presents with a different and more severe phenotype than older-onset IBD. We investigated whether patients with VEO-IBD carry rare or novel variants in genes associated with immunodeficiencies that might contribute to disease development. METHODS: Patients with VEO-IBD and parents (when available) were recruited from the Children's Hospital of Philadelphia from March 2013 through July 2014. We analyzed DNA from 125 patients with VEO-IBD (age, 3 wk to 4 y) and 19 parents, 4 of whom also had IBD. Exome capture was performed by Agilent SureSelect V4, and sequencing was performed using the Illumina HiSeq platform. Alignment to human genome GRCh37 was achieved followed by postprocessing and variant calling. After functional annotation, candidate variants were analyzed for change in protein function, minor allele frequency less than 0.1%, and scaled combined annotation-dependent depletion scores of 10 or less. We focused on genes associated with primary immunodeficiencies and related pathways. An additional 210 exome samples from patients with pediatric IBD (n = 45) or adult-onset Crohn's disease (n = 20) and healthy individuals (controls, n = 145) were obtained from the University of Kiel, Germany, and used as control groups. RESULTS: Four hundred genes and regions associated with primary immunodeficiency, covering approximately 6500 coding exons totaling more than 1 Mbp of coding sequence, were selected from the whole-exome data. Our analysis showed novel and rare variants within these genes that could contribute to the development of VEO-IBD, including rare heterozygous missense variants in IL10RA and previously unidentified variants in MSH5 and CD19. CONCLUSIONS: In an exome sequence analysis of patients with VEO-IBD and their parents, we identified variants in genes that regulate B- and T-cell functions and could contribute to pathogenesis. Our analysis could lead to the identification of previously unidentified IBD-associated variants.

Utility and limitations of exome sequencing as a genetic diagnostic tool for conditions associated with pediatric sudden cardiac arrest/sudden cardiac death.

BACKGROUND: Conditions associated with sudden cardiac arrest/death (SCA/D) in youth often have a genetic etiology. While SCA/D is uncommon, a pro-active family screening approach may identify these inherited structural and electrical abnormalities prior to symptomatic events and allow appropriate surveillance and treatment. This study investigated the diagnostic utility of exome sequencing (ES) by evaluating the capture and coverage of genes related to SCA/D. METHODS: Samples from 102 individuals (13 with known molecular etiologies for SCA/D, 30 individuals without known molecular etiologies for SCA/D and 59 with other conditions) were analyzed following exome capture and sequencing at an average read depth of 100X. Reads were mapped to human genome GRCh37 using Novoalign, and post-processing and analysis was done using Picard and GATK. A total of 103 genes (2,190 exons) related to SCA/D were used as a primary filter. An additional 100 random variants within the targeted genes associated with SCA/D were also selected and evaluated for depth of sequencing and coverage. Although the primary objective was to evaluate the adequacy of depth of sequencing and coverage of targeted SCA/D genes and not for primary diagnosis, all patients who had SCA/D (known or unknown molecular etiologies) were evaluated with the project's variant analysis pipeline to determine if the molecular etiologies could be successfully identified. RESULTS: The majority of exons (97.6 %) were captured and fully covered on average at minimum of 20x sequencing depth. The proportion of unique genomic positions reported within poorly covered exons remained small (4 %). Exonic regions with less coverage reflect the need to enrich these areas to improve coverage. Despite limitations in coverage, we identified 100 % of cases with a prior known molecular etiology for SCA/D, and analysis of an additional 30 individuals with SCA/D but no known molecular etiology revealed a diagnostic answer in 5/30 (17 %). We also demonstrated 95 % of 100 randomly selected reported variants within our targeted genes would have been picked up on ES based on our coverage analysis. CONCLUSIONS: ES is a helpful clinical diagnostic tool for SCA/D given its potential to successfully identify a molecular diagnosis, but clinicians should be aware of limitations of available platforms from technical and diagnostic perspectives.

Efficient digest of high-throughput sequencing data in a reproducible report.

BACKGROUND: High-throughput sequencing (HTS) technologies are spearheading the accelerated development of biomedical research. Processing and summarizing the large amount of data generated by HTS presents a non-trivial challenge to bioinformatics. A commonly adopted standard is to store sequencing reads aligned to a reference genome in SAM (Sequence Alignment/Map) or BAM (Binary Alignment/Map) files. Quality control of SAM/BAM files is a critical checkpoint before downstream analysis. The goal of the current project is to facilitate and standardize this process. RESULTS: We developed bamchop, a robust program to efficiently summarize key statistical metrics of HTS data stored in BAM files, and to visually present the results in a formatted report. The report documents information about various aspects of HTS data, such as sequencing quality, mapping to a reference genome, sequencing coverage, and base frequency. Bamchop uses the R language and Bioconductor packages to calculate statistical matrices and the Sweave utility and associated LaTeX markup for documentation. Bamchop's efficiency and robustness were tested on BAM files generated by local sequencing facilities and the 1000 Genomes Project. Source code, instruction and example reports of bamchop are freely available from https://github.com/CBMi-BiG/bamchop. CONCLUSIONS: Bamchop enables biomedical researchers to quickly and rigorously evaluate HTS data by providing a convenient synopsis and user-friendly reports.

Filtering error from SOLiD Output.

SUMMARY: Here, we report the development of a filtering framework designed for efficient identification of both polyclonal and independent errors within SOLiD sequence data. The filtering utilizes the quality values reported by SOLiD's primary analysis for the identification of the two different types of errors. The filtering framework facilitates the passage of high-quality data into a variety of functional genomics applications, including de novo assemblers and sequence matching programs for SNP calling, improving the output quality and reducing resources necessary for analysis. AVAILABILITY: This error analysis framework is written in Perl and runs on Mac OS and Linux/Unix systems. The filter, documentation and sample Excel files for quality analysis are available at http://hts.rutgers.edu/filter and are distributed as Open Source software under the GPLv3.0. CONTACT: tmichael@waksman.rutgers.edu SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

Misannotated Multi-Nucleotide Variants in Public Cancer Genomics Datasets Lead to Inaccurate Mutation Calls with Significant Implications.

Although next-generation sequencing is widely used in cancer to profile tumors and detect variants, most somatic variant callers used in these pipelines identify variants at the lowest possible granularity, single-nucleotide variants (SNV). As a result, multiple adjacent SNVs are called individually instead of as a multi-nucleotide variants (MNV). With this approach, the amino acid change from the individual SNV within a codon could be different from the amino acid change based on the MNV that results from combining SNV, leading to incorrect conclusions about the downstream effects of the variants. Here, we analyzed 10,383 variant call files (VCF) from the Cancer Genome Atlas (TCGA) and found 12,141 incorrectly annotated MNVs. Analysis of seven commonly mutated genes from 178 studies in cBioPortal revealed that MNVs were consistently missed in 20 of these studies, whereas they were correctly annotated in 15 more recent studies. At the BRAF V600 locus, the most common example of MNV, several public datasets reported separate BRAF V600E and BRAF V600M variants instead of a single merged V600K variant. VCFs from the TCGA Mutect2 caller were used to develop a solution to merge SNV to MNV. Our custom script used the phasing information from the SNV VCF and determined whether SNVs were at the same codon and needed to be merged into MNV before variant annotation. This study shows that institutions performing NGS sequencing for cancer genomics should incorporate the step of merging MNV as a best practice in their pipelines. SIGNIFICANCE: Identification of incorrect mutation calls in TCGA, including clinically relevant BRAF V600 and KRAS G12, will influence research and potentially clinical decisions.

Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank.

The UK Biobank Exome Sequencing Consortium (UKB-ESC) is a private-public partnership between the UK Biobank (UKB) and eight biopharmaceutical companies that will complete the sequencing of exomes for all ~500,000 UKB participants. Here, we describe the early results from ~200,000 UKB participants and the features of this project that enabled its success. The biopharmaceutical industry has increasingly used human genetics to improve success in drug discovery. Recognizing the need for large-scale human genetics data, as well as the unique value of the data access and contribution terms of the UKB, the UKB-ESC was formed. As a result, exome data from 200,643 UKB enrollees are now available. These data include ~10 million exonic variants-a rich resource of rare coding variation that is particularly valuable for drug discovery. The UKB-ESC precompetitive collaboration has further strengthened academic and industry ties and has provided teams with an opportunity to interact with and learn from the wider research community.

Modeling performance of sample collection sites using whole exome sequencing metrics.

Although next-generation sequencing assays are routinely carried out using samples from cancer trials, the sequencing data are not always of the required quality. There is a need to evaluate the performance of tissue collection sites and provide feedback about the quality of next-generation sequencing data. This study used a modeling approach based on whole exome sequencing quality control (QC) metrics to evaluate the relative performance of sites participating in the Bristol Myers Squibb Immuno-Oncology clinical trials sample collection. We identified several events for the sample swap. Overall, most sites performed well and few showed poor performance. These findings can increase awareness of sample failure and improve the quality of samples.

Degradation of the extracellular matrix is part of the pathology of ulcerative colitis.

The scientific value of re-analyzing existing datasets is often proportional to the complexity of the data. Proteomics data are inherently complex and can be analyzed at many levels, including proteins, peptides, and post-translational modifications to verify and/or develop new hypotheses. In this paper, we present our re-analysis of a previously published study comparing colon biopsy samples from ulcerative colitis (UC) patients to non-affected controls. We used a different statistical approach, employing a linear mixed-effects regression model and analyzed the data both on the protein and peptide level. In addition to confirming and reinforcing the original finding of upregulation of neutrophil extracellular traps (NETs), we report novel findings, including that Extracellular Matrix (ECM) degradation and neutrophil maturation are involved in the pathology of UC. The pharmaceutically most relevant differential protein expressions were confirmed using immunohistochemistry as an orthogonal method. As part of this study, we also compared proteomics data to previously published mRNA expression data. These comparisons indicated compensatory regulation at transcription levels of the ECM proteins we identified and open possible new avenues for drug discovery.

Bioinformatic Methods and Bridging of Assay Results for Reliable Tumor Mutational Burden Assessment in Non-Small-Cell Lung Cancer.

INTRODUCTION: Tumor mutational burden (TMB) has emerged as a clinically relevant biomarker that may be associated with immune checkpoint inhibitor efficacy. Standardization of TMB measurement is essential for implementing diagnostic tools to guide treatment. OBJECTIVE: Here we describe the in-depth evaluation of bioinformatic TMB analysis by whole exome sequencing (WES) in formalin-fixed, paraffin-embedded samples from a phase III clinical trial. METHODS: In the CheckMate 026 clinical trial, TMB was retrospectively assessed in 312 patients with non-small-cell lung cancer (58% of the intent-to-treat population) who received first-line nivolumab treatment or standard-of-care chemotherapy. We examined the sensitivity of TMB assessment to bioinformatic filtering methods and assessed concordance between TMB data derived by WES and the FoundationOne® CDx assay. RESULTS: TMB scores comprising synonymous, indel, frameshift, and nonsense mutations (all mutations) were 3.1-fold higher than data including missense mutations only, but values were highly correlated (Spearman's r = 0.99). Scores from CheckMate 026 samples including missense mutations only were similar to those generated from data in The Cancer Genome Atlas, but those including all mutations were generally higher. Using databases for germline subtraction (instead of matched controls) showed a trend for race-dependent increases in TMB scores. WES and FoundationOne CDx outputs were highly correlated (Spearman's r = 0.90). CONCLUSIONS: Parameter variation can impact TMB calculations, highlighting the need for standardization. Encouragingly, differences between assays could be accounted for by empirical calibration, suggesting that reliable TMB assessment across assays, platforms, and centers is achievable.

Tumor Mutational Burden and Efficacy of Nivolumab Monotherapy and in Combination with Ipilimumab in Small-Cell Lung Cancer.

Durable responses and encouraging survival have been demonstrated with immune checkpoint inhibitors in small-cell lung cancer (SCLC), but predictive markers are unknown. We used whole exome sequencing to evaluate the impact of tumor mutational burden on efficacy of nivolumab monotherapy or combined with ipilimumab in patients with SCLC from the nonrandomized or randomized cohorts of CheckMate 032. Patients received nivolumab (3 mg/kg every 2 weeks) or nivolumab plus ipilimumab (1 mg/kg plus 3 mg/kg every 3 weeks for four cycles, followed by nivolumab 3 mg/kg every 2 weeks). Efficacy of nivolumab ± ipilimumab was enhanced in patients with high tumor mutational burden. Nivolumab plus ipilimumab appeared to provide a greater clinical benefit than nivolumab monotherapy in the high tumor mutational burden tertile.

STK11/LKB1 Mutations and PD-1 Inhibitor Resistance in KRAS-Mutant Lung Adenocarcinoma.

KRAS is the most common oncogenic driver in lung adenocarcinoma (LUAC). We previously reported that STK11/LKB1 (KL) or TP53 (KP) comutations define distinct subgroups of KRAS-mutant LUAC. Here, we examine the efficacy of PD-1 inhibitors in these subgroups. Objective response rates to PD-1 blockade differed significantly among KL (7.4%), KP (35.7%), and K-only (28.6%) subgroups (P < 0.001) in the Stand Up To Cancer (SU2C) cohort (174 patients) with KRAS-mutant LUAC and in patients treated with nivolumab in the CheckMate-057 phase III trial (0% vs. 57.1% vs. 18.2%; P = 0.047). In the SU2C cohort, KL LUAC exhibited shorter progression-free (P < 0.001) and overall (P = 0.0015) survival compared with KRASMUT;STK11/LKB1WT LUAC. Among 924 LUACs, STK11/LKB1 alterations were the only marker significantly associated with PD-L1 negativity in TMBIntermediate/High LUAC. The impact of STK11/LKB1 alterations on clinical outcomes with PD-1/PD-L1 inhibitors extended to PD-L1-positive non-small cell lung cancer. In Kras-mutant murine LUAC models, Stk11/Lkb1 loss promoted PD-1/PD-L1 inhibitor resistance, suggesting a causal role. Our results identify STK11/LKB1 alterations as a major driver of primary resistance to PD-1 blockade in KRAS-mutant LUAC.Significance: This work identifies STK11/LKB1 alterations as the most prevalent genomic driver of primary resistance to PD-1 axis inhibitors in KRAS-mutant lung adenocarcinoma. Genomic profiling may enhance the predictive utility of PD-L1 expression and tumor mutation burden and facilitate establishment of personalized combination immunotherapy approaches for genomically defined LUAC subsets. Cancer Discov; 8(7); 822-35. ©2018 AACR.See related commentary by Etxeberria et al., p. 794This article is highlighted in the In This Issue feature, p. 781.

Dominant form of congenital hyperinsulinism maps to HK1 region on 10q.

BACKGROUND/AIMS: In a family with congenital hyperinsulinism (HI), first described in the 1950s by McQuarrie, we examined the genetic locus and clinical phenotype of a novel form of dominant HI. METHODS: We surveyed 25 affected individuals, 7 of whom participated in tests of insulin dysregulation (24-hour fasting, oral glucose and protein tolerance tests). To identify the disease locus and potential disease-associated mutations we performed linkage analysis, whole transcriptome sequencing, whole genome sequencing, gene capture, and next generation sequencing. RESULTS: Most affecteds were diagnosed with HI before age one and 40% presented with a seizure. All affecteds responded well to diazoxide. Affecteds failed to adequately suppress insulin secretion following oral glucose tolerance test or prolonged fasting; none had protein-sensitive hypoglycemia. Linkage analysis mapped the HI locus to Chr10q21-22, a region containing 48 genes. Three novel noncoding variants were found in hexokinase 1 (HK1) and one missense variant in the coding region of DNA2. CONCLUSION: Dominant, diazoxide-responsive HI in this family maps to a novel locus on Chr10q21-22. HK1 is the more attractive disease gene candidate since a mutation interfering with the normal suppression of HK1 expression in beta-cells could readily explain the hypoglycemia phenotype of this pedigree.

Efficient calculation of molecular properties from simulation using kernel molecular dynamics.

Understanding the relationship between chemical structure and function is a ubiquitous problem within the fields of chemistry and biology. Simulation approaches attack the problem utilizing physics to understand a given process at the particle level. Unfortunately, these approaches are often too expensive for many problems of interest. Informatics approaches attack the problem with empirical analysis of descriptions of chemical structure. The issue in these methods is how to describe molecules in a manner that facilitates accurate and general calculation of molecular properties. Here, we present a novel approach that utilizes aspects of simulation and informatics in order to formulate structure-property relationships. We show how supervised learning can be utilized to overcome the sampling problem in simulation approaches. Likewise, we show how learning can be achieved based on molecular descriptions that are rooted in the physics of dynamic intermolecular forces. We apply the approach to three problems including the analysis of corticosteroid binding globulin ligand binding affinity, identification of formylpeptide receptor ligands, and identification of resveratrol analogues capable of inhibiting activation of transcription factor nuclear factor kappaB.